- Cute! and Furry!
- In fact … the furriest: 150,000 hairs / cm2 (>1,000,000 / in2)
11/22/2021
(Estes et al. 1974)
(Smith et al. 2021)
300,000 in 1740 … < 2,000 in 1900.
… the rush for the otters’ “soft gold” was a predictable boom and bust cycle, a cautionary example of unsustainable resource use, and a socioeconomic driver of Western—mainly American—involvement in the Pacific region starting in the eighteenth century. (Loshbaugh 2021)
Remnant populations from Aleutian Islands … released in OR, WA, BC and SE-AK 1969 – 1972.
This is a closed population … and what we will be (mainly) dealing with for the next 3 weeks.
Source:
https://wdfw.wa.gov/species-habitats/species/enhydra-lutris-kenyoni#resources
Load data:
WA <- read.csv("data/WA_SeaOtters_PopGrowth.csv")
head(WA)
## year count ## 1 1970 59 ## 2 1989 208 ## 3 1990 212 ## 4 1991 276 ## 5 1992 313 ## 6 1993 307
plot(WA)
plot(WA); abline(lm(count ~ year, data = WA))
plot(WA, log = "y")
WA.growth <- lm(log(count) ~ I(year-1970), data = WA) summary(WA.growth)
## ## Call: ## lm(formula = log(count) ~ I(year - 1970), data = WA) ## ## Residuals: ## Min 1Q Median 3Q Max ## -0.191084 -0.062944 -0.005104 0.055518 0.231704 ## ## Coefficients: ## Estimate Std. Error t value Pr(>|t|) ## (Intercept) 4.082641 0.073024 55.91 <2e-16 *** ## I(year - 1970) 0.073251 0.002367 30.95 <2e-16 *** ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ## ## Residual standard error: 0.1094 on 23 degrees of freedom ## Multiple R-squared: 0.9766, Adjusted R-squared: 0.9755 ## F-statistic: 958.1 on 1 and 23 DF, p-value: < 2.2e-16
par(mfrow = c(1,2)) plot(WA.growth, 1:2)
#require(ggplot2) #ggplot(WA, aes(year, count)) + geom_point() + geom_smooth(method = "glm", #family = "log")
\[\log(N_i) = \alpha + \beta \, Y_i\] \[N_i = \exp(\alpha) \times \exp(\beta \, Y_i)\] \[N_i = e^\alpha {e^\beta}^{Y_i}\] \[N_i = N_0 \lambda ^ {Y_i}\]
where \(N_0 = e^{\alpha} = e^{4.08} = 59.14\), and \(\lambda = e^{\beta} = e^{0.07325} = 1.076\).
SO … percent rate of growth is about 7.6%!
plot(count~year, data = WA) curve(59.14 * exp(0.07325 * (x-1970)), add = TRUE, col = 2, lwd = 2)